automated data cleaning tool
Automated Data Cleaning Tool.
The main goal is to develop a Python tool
datacleanbot such that:
Given a random parsed raw dataset representing a supervised learning problem, the Python tool is capable of automatically identifying the potential issues and reporting the results and recommendations to the end-user in an effective way.
$ pip install datacleanbot
Install OpenML (version 0.9.0):
OpenML is used to easily import datasets and share models and experiments.
$ pip install openml
For Windows, you need to have C++ Compiler installed.
Acquire data from OpenML:
>>> import openml as oml >>> data = oml.datasets.get_dataset(id) # id: openml dataset id >>> X, y, categorical_indicator, features = data.get_data(target=data.default_target_attribute, dataset_format='array') >>> Xy = np.concatenate((X,y.reshape((y.shape,1))), axis=1)
Autoclean data with datacleanbot:
>>> import datacleanbot.dataclean as dc >>> Xy = dc.autoclean(Xy, data.name, features)
datacleanbot is equipped with the following capabilities:
- Present an overview report of the given dataset
- The most important features
- Statistical information (e.g., mean, max, min)
- Data types of features
- Clean common data problems in the raw dataset
- Duplicated records
- Inconsistent column names
- Missing values
The two aspects
datacleanbot meaningfully automates are marked in bold.
The user's guide can be found at datacleanbot.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for datacleanbot-0.91-py3-none-any.whl